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英文翻译姓名：张衡学号：1102080234指导教师：孙中桥专业：信息管理与信息系统班级：2011级时间：2015年6月25日信息管理系-1-信息管理系英文翻译评价表学生姓名张衡性别男学号1102080234外文文献标题FastComponent-BasedQRCodeDetectioninArbitrarilyAcquiredImages外文文献出处以下内容由指导教师填写（打勾“√”选择）评价项目评价结论打勾评价结论打勾评价结论打勾是否外文期刊文献是否与本人论文相关完全相关一般不相关翻译工作量超负荷饱和不饱和翻译态度认真一般不认真翻译进度按计划执行一般未按计划执行翻译训练效果优良中差综合评语（是否完成了规定任务、效果是否符合要求等）指导教师签名：2015年4月25日制表：李铁治注1：此表与翻译文本一起装订；注2：为了加强学生外语应用能力的训练，每位同学至少选择毕业论文中一篇外文参考文献（10000英文字符），翻译成中文。外文文献及中文译文不装钉进论文中，只形成单行本放入档案袋即可。-2--3-英文原文FastComponent-BasedQRCodeDetectioninArbitrarilyAcquiredImagesAbstractQuickResponse(QR)codesareatypeof2Dbar-codethatisbecomingverypopular,withseveralapplicationpossibilities.Sincetheycanencodealphanumericcharac-ters,arichsetofinformationcanbemadeavailablethroughencodedURLaddresses.Inparticular,QRcodescouldbeusedtoaidvisuallyimpairedandblindpeopletoaccesswebbasedvoiceinformationsystemsandservices,andau-tonomousrobotstoacquirecontext-relevantinformation.However,inordertobedecoded,QRcodesneedtobeprop-erlyframed,somethingthatrobots,visuallyimpairedandblindpeoplewillnotbeabletodoeasilywithoutguid-ance.Therefore,anyapplicationthataimsassistingrobotsorvisuallyimpairedpeoplemusthavethecapabilitytode-tectQRcodesandguidethemtoproperlyframethecode.Afastcomponent-basedtwo-stageapproachfordetectingQRcodesinarbitrarilyacquiredimagesisproposedinthiswork.Inthefirststage,regularcomponentspresentatthreecornersofthecodearedetected,andinthesecondstagege-ometricalrestrictionsamongdetectedcomponentsareveri-fiedtoconfirmthepresenceofacode.Experimentalresultsshowahighdetectionrate,superiorto90%,atafastspeedcompatiblewithreal-timeapplications.KeywordsQRcode•Component-baseddetection•Haar-likefeatures•Cascadeclassifier1IntroductionComparedtotraditional1Dbarcodes,2Dbarcodescanen-codealargeramountofdata,includingalphanumericchar-acters.QRcode,whichstandsforQuickResponseCode,isatypeoftwo-dimensionalcodeintroducedbyDensoWavein1994[12].Theyhavebeendesignedtobeeasilyfoundandtohaveitssizeandorientationdeterminedunderbadimagingconditions.Inaddition,ISO/IEC18004specifiesanerrorcorrectionschemethatcanrecoveratmost30%ofoccludedordamagedsymbolarea.ThesefeaturesmaketheQRcodeanextremelywellsucceededtechnologyintheareaofbarcodes.Figure1showssomeexamplesofQRcodes.-4-Fig.1SamplesofQRcodeQRcodeswereinitiallyusedbytheautomotiveindustrytotrackvehiclepartsduringthemanufacturingprocess[12].Nowadays,QRcodesaremostcommonlyusedas“physicalhyperlinks”(encodedhyperlinks),notablyintheadvertisingindustry,toconnectplacesandobjectstowebsitescontain-ingadditionalcontextrelevantinformation.Applicationsineducationandentertainment[8,23,25],dataandsystemse-curity[9,16,19],specificserviceoffer[7,27],amongothersarealsoemerging.Anotherpossibleapplicationconsistsinhelpingvisuallyimpairedandblindpeople,orevenrobots,inseveraltaskssuchasindoornavigation,shopping,read-ing,andmuchmore[2,11,13].Theexistingdecoders,easilyfoundformobiledevices,areabletoworkcorrectlyonlyifcodesareproperlyframed,withcoderegioncorrespondingtoatleast30%oftheim-age.Whenexploringanenvironment,visuallyimpairedorrobotswillnotbeabletocapturesuchimagesunlesstheyaretoldwherethosecodesarelocated.Thus,inordertomakeusefulapplicationsforthemviable,detectingthepresenceofacodeinanimageisanecessarysteppriortothedecod-ingprocess.Inrelatedliterature,themajorityofworkthatmentionQRcoderecognitionordetectionisactuallyconcernedwithimprovingimagequalityordeterminingtheexactcontoursofthecoderatherthanfinding(i.e,decidingwhetherthereisorthereisnot)aQRcodesymbolinanimage[10,20,22].Infact,mostoftheimagesconsideredinthoseworksare-5-imagesacquiredwiththespecificintentofcapturingonlythecodesymbol.SomefewworksthatdealwiththeproblemoffindingQRcodesproposesolutionsthatrelyonauxiliaryinforma-tionsuchasvisualcuesorRFIDtags[11,28].Althoughsuchapproachispossibleincontrolledenvironments,itisnotpracticalingeneralcontexts.ThisworkaddressestheproblemofdetectingQRcodesinarbitrarilyacquiredimageswithoutrelyingonauxiliarycues.Theaimisnotonlytodetectthepresenceofcodesymbols,butalsotodelimittheirpositionwithinanimageasaccuratelyaspossible.Thatwouldallow,additionally,toinstructanuserorrobottoproperlyapproachthecameratowardsthecode.Tothatend,acomponent-based,two-stagedetectionap-proachisproposed.Inthefirststage,asquarepattern,calledfinderpattern(FIP),locatedatthreecornersofanyQRcodeisdetectedusingacascadedclassifiertrainedaccordingtotherapidobjectdetectionmethodproposedin[26].Inthesecondstage,geometricalrelationshipsamongdetectedcan-didatefinderpatternsareanalyzedinordertoverifyiftherearesubgroupsofthreeofthemspatiallyarrangedasthreecornersofasquareregion.Areportonpreliminaryresultsofthisapproachhasbeenpreviouslypublishedin[3].Inthispaper,areformulatedreportoftheapproachandanextendedsetofresultsarepresented.Inparticular,adetailedaccountofthesecondstage,includingaformaldescriptionofthecomponentaggregationalgorithm,newresultsobtainedwithalargertrainingandtestsets,discussionsconcerningaddi-tionalparametersthathavenotbeenexaminedintheprevi-ouswork,andquantitativeresults,notpresentedbefore,onQRcodedetect